Thesis topic proposal
Péter Horváth
3D image analysis and machine learning methods using deep learning for understanding neuron cell communication


Institute: University of Szeged
computer sciences
PhD School in Computer Science

Thesis supervisor: Péter Horváth
Location of studies (in Hungarian): University of Szeged
Abbreviation of location of studies: SZTE

Description of the research topic:

Understanding cellular complexity is a key element of better disease treatment and answering fundamental questions of biology (Horvath, 2016, Nature Drug Disc., 15, 751). Recent advancements in systems microscopy and computational cell biology made possible to automatically and objectively analyse images even in scales as large as millions of images and billions of cells (Badertscher, 2016, Cell Reports, 13, 12, 2879). One of the most demanding of these tasks is to analyse label-free 3D microscopy images at the single-cell level. The aim of this project is to develop a system that first corrects imaging problems caused by imperfections of the microscope’s optical system (Smith, 2015, Nature Methods, 12, 404). On the corrected images, we perform a reconstruction step that reverts the imaging model of the microscope and the result is suitable for image segmentation. Cellular phenotypes are identified using deep machine learning algorithms. We presented an automated patch clamp control method, that calibrates the needle (Koos, SCIA, 2017, accepted) and navigates it to an arbitrary position. The aim will be that using this system automatically target a single neuron cells and measure their communication.

Required language skills: english
Number of students who can be accepted: 1

Deadline for application: 2018-09-30

All rights reserved © 2007, Hungarian Doctoral Council. Doctoral Council registration number at commissioner for data protection: 02003/0001. Program version: 2.2358 ( 2017. X. 31. )